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of 42
pro vyhledávání: '"Song, Peiyang"'
Autor:
Kumarappan, Adarsh, Tiwari, Mo, Song, Peiyang, George, Robert Joseph, Xiao, Chaowei, Anandkumar, Anima
Large Language Models (LLMs) have been successful in mathematical reasoning tasks such as formal theorem proving when integrated with interactive proof assistants like Lean. Existing approaches involve training or fine-tuning an LLM on a specific dat
Externí odkaz:
http://arxiv.org/abs/2410.06209
As a type of figurative language, an East Asian idiom condenses rich cultural background into only a few characters. Translating such idioms is challenging for human translators, who often resort to choosing a context-aware translation from an existi
Externí odkaz:
http://arxiv.org/abs/2410.00988
Recent advancements in artificial intelligence have led to the creation of highly capable large language models (LLMs) that can perform tasks in a human-like manner. However, LLMs exhibit only infant-level cognitive abilities in certain areas. One su
Externí odkaz:
http://arxiv.org/abs/2409.15454
Theorem proving is an important challenge for large language models (LLMs), as formal proofs can be checked rigorously by proof assistants such as Lean, leaving no room for hallucination. Existing LLM-based provers try to prove theorems in a fully au
Externí odkaz:
http://arxiv.org/abs/2404.12534
Autor:
Yang, Kaiyu, Swope, Aidan M., Gu, Alex, Chalamala, Rahul, Song, Peiyang, Yu, Shixing, Godil, Saad, Prenger, Ryan, Anandkumar, Anima
Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean. However, existing methods are difficult to reproduce or build on, due to private code, data, and large compute requirements. This has crea
Externí odkaz:
http://arxiv.org/abs/2306.15626
Autor:
Song, Peiyang
The development of the industrial Internet of Things (IoT) calls for higher spectrum efficiency (SE). Faster than Nyquist (FTN) and non-orthogonal multiple access (NOMA) are both promising paradigms to improve the SE without any extra spectrum resour
Externí odkaz:
http://arxiv.org/abs/2207.02653
Publikováno v:
In The British Accounting Review November 2024 56(6) Part A
With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. Thi
Externí odkaz:
http://arxiv.org/abs/2008.00162
Publikováno v:
Electronics Letters 55.21 (2019): 1155-1157
This letter proposes a blind symbol packing rartio estimation for faster-than-Nyquist (FTN) signaling based on state-of-the-art deep learning (DL) technology. The symbol packing rartio is a vital parameter to obtain the real symbol rate and recover t
Externí odkaz:
http://arxiv.org/abs/1907.05606
Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signal
Externí odkaz:
http://arxiv.org/abs/1811.02764